\nomenclature[a]{$\bm{\alpha},\bm{\alpha}^*,\bm{\beta},\bm{\beta}^*$}{
$\ell$-dimensional vectors of Lagrange multipliers.}%
\nomenclature[b]{$\gamma$}{Parameter involving the RBF kernel parameter
  $\sigma$.}%
\nomenclature[c]{$\delta$}{Tolerance for consecutive global best fitness
  values.}%
\nomenclature[d]{$\varepsilon$}{``Radius'' of the ``tube'' defined in the
  Vapnik's $\varepsilon$-insensitive loss function.}%
\nomenclature[e]{$\theta$}{Reliability growth factor.}%
\nomenclature[f]{$\vartheta$}{Number of points in the validation set.}%
\nomenclature[g]{$\lambda$}{Number of points in the test set.}%
\nomenclature[h]{$\bm{\xi},\bm{\xi}^*$}{$\ell$-dimensional vectors of
  slack variabies.}%
\nomenclature[i]{$\rho$}{Angle between $\mathbf{x}_{SV,-1}$ and
  $\mathbf{w}$.}%
\nomenclature[j]{$\sigma$}{RBF kernel parameter}%
\nomenclature[k]{$\Phi$}{Mapping $\mathbb{R}^n \rightarrow \cal F$.}%
\nomenclature[l]{$\varphi$}{Sum of PSO parameters $c_1$ and $c_2$.}%
\nomenclature[m]{$\chi$}{Constriction factor}%
\nomenclature[n]{$\omega$}{Angle between $\mathbf{x}_{SV,+1}$ and
  $\mathbf{w}$.}%
\nomenclature[o]{$b$}{Linear coefficient of $H$.}%
\nomenclature[p]{$bIter$}{PSO iteration in which the best particle was
  found.}%
\nomenclature[q]{$C$}{Trade-off between training error and machine
  capacity.}%
\nomenclature[r]{$c_1,c_2$}{PSO parameters.}%
\nomenclature[s]{$D$}{Training data set.}%
\nomenclature[t]{$d$}{Degree of a polynomial}%
\nomenclature[u]{$d(\mathbf{x})$}{Decision function.}%
\nomenclature[v]{$\cal F$}{Feature space.}%
\nomenclature[w]{$f(\mathbf{x})$}{Mapping or function of $\mathbf{x}$.}%
\nomenclature[x]{$f_i$}{Validation fitness value associated with the
  $i^{th}$ particle.}%
\nomenclature[y]{$f_{test}$}{Test fitness value associated with the best
  particle.}%
\nomenclature[z]{$g(\mathbf{x})$}{Non-zero function that satisfies $\int
  g^2(\mathbf{x})d\mathbf{x} < \infty$.}%
\nomenclature[za]{$H$}{Separating hyperplane.}%
\nomenclature[zb]{$h$}{Test point index.}%
\nomenclature[zc]{$H_-, H_+$}{Lower and upper hyperplanes that define the
  margin.}%
\nomenclature[zd]{$i$}{Training point index; particle index.}%
\nomenclature[ze]{$j$}{Auxiliar index; dimension index of a vector.}%
\nomenclature[zf]{$K$}{Kernel function.}%
\nomenclature[zg]{$k$}{Category index of a multi-classification problem;
  number of steps ahead minus one in a time series prediction; number
  of subsets in cross-validation.}%
\nomenclature[zh]{${\mathcal L}$}{Lagrangian function.}%
\nomenclature[zi]{${\mathcal L}_d$}{Dual Lagrangian function.}%
\nomenclature[zj]{$L$}{Loss function.}%
\nomenclature[zk]{$\ell$}{Number of training points $(\mathbf{x}_i,y_i)$.}%
\nomenclature[zl]{$M$}{Margin.}%
\nomenclature[zm]{$m$}{Number of categories of a multi-classification
  problem.}%
\nomenclature[zn]{$n$}{Dimension of $\mathbf{x}$; number of variables.}%
\nomenclature[zo]{$nFSV$}{Number of free support vectors.}%
\nomenclature[zp]{$nIter$}{Maximum number of PSO iterations.}%
\nomenclature[zq]{$nLFSV, nUFSV$}{Number of free support vectors lying on
  $H_-$ and on $H_+$ (regression case).}%
\nomenclature[zr]{$nNeigh$}{Number of particles' neihgbors.}%
\nomenclature[zs]{$nPart$}{Number of particles.}%
\nomenclature[zt]{$nSV$}{Number of support vectors.}%
\nomenclature[zu]{$p$}{Number of lagged variables in a time series,
  dimension of $\mathbf{x}_t$.}%
\nomenclature[zv]{$\mathbf{p}$}{The best individual position a particle
  has found so far.}%
\nomenclature[zw]{$\mathbf{p}_g$}{Best position in the neighborhood of a
  particle.}%
\nomenclature[zx]{$\mathbb R$}{Set of real numbers.}%
\nomenclature[zy]{$r$}{Number of categories associated with a categorical
  variable.}%
\nomenclature[zz]{$s$}{Support vector index.}%
\nomenclature[zza]{$T$}{Total operational time of an equipment or matrix
  transpose operation.}%
\nomenclature[zzb]{$t$}{Time index.}%
\nomenclature[zzc]{$u_1,u_2$}{Random numbers generated by a uniform
  distribution defined in the interval [0,1].}%
\nomenclature[zzd]{$v_{max}$}{Maximum velocity.}%
\nomenclature[zze]{$\mathbf{v}$}{Velocity vector.}%
\nomenclature[zzf]{$\mathbf{v}^{max}$}{Maximum velocity a particle can have
  associated with the $j^{th}$ dimension.}%
\nomenclature[zzg]{$w$}{Inertia weight.}%
\nomenclature[zzh]{$\mathbf{w}$}{Normal vector in relation to $H$.}%
\nomenclature[zzi]{$x^{ind}$}{Indicator variable.}%
\nomenclature[zzj]{$x_{min,j}, x_{max,j}$}{Minimum and maximum training values
of the $j^{\text{th}}$ dimension of $\mathbf{x}_i$.}%
\nomenclature[zzk]{$\mathbf{x}$}{Multidimensional input vector; current
  particle position}%
\nomenclature[zzl]{$\mathbf{x}^{min},\mathbf{x}^{max}$}{$n$-dimensional
  vectors of variables' bounds.}%
\nomenclature[zzm]{$\mathbf{x}_{SV,-1}, \mathbf{x}_{SV,+1}$}{Support vector
  from the negative and positive classes.}%
\nomenclature[zzn]{$y$}{Output value.}%
\nomenclature[zzo]{$\hat{y}$}{Predicted output value.}%
\nomenclature[zzp]{$y_{min},y_{max}$}{Minimum and maximum training values of the
output $y$.}%